Professor | 徐山 | 数字化转型顾问

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Name	徐山
Contact Information	xushan@njau.edu.cn
Language ability	英语, 中文
Province	江苏

Upload Avatar (500 x 500)

Name

徐山

Contact Information

xushan@njau.edu.cn

Language ability

英语, 中文

Province

江苏

University/Lab Affiliation and Title	南京农业大学
Faculty	academy for adyanced interdisciplinary studies
Academic Background & Achievements	2015.9-2021.6: 北京师范大学，地图学与地理信息系统专业，理学博士 2019.1-2020.1: 赫尔辛基大学，联合培养博士生 2008.9-2012.6: 华中农业大学，地理信息系统专业，理学学士

University/Lab Affiliation and Title

南京农业大学

Faculty

academy for adyanced interdisciplinary studies

Academic Background & Achievements

2015.9-2021.6: 北京师范大学，地图学与地理信息系统专业，理学博士
2019.1-2020.1: 赫尔辛基大学，联合培养博士生
2008.9-2012.6: 华中农业大学，地理信息系统专业，理学学士

Areas of Focus	植被定量遥感与农作物生长监测基于光谱的农作物生理表型提取
Publications	Exploring the Sensitivity of Solar-Induced Chlorophyll Fluorescence at Different Wavelengths in Response to Drought, Xu, S., Liu, Z., Han, S., Chen, Z., He, X., Zhao, H., & Ren, S., 2023 Structural and photosynthetic dynamics mediate the response of SIF to water stress in a potato crop, Xu, S., Atherton, J., Riikonen, A., Zhang, C., Oivukkamäki, J., MacArthur, A., ... & Porcar-Castell, A., 2021 On the estimation of the leaf angle distribution from drone based photogrammetry, Xu, S., Zaidan, M. A., Honkavaara, E., Hakala, T., Viljanen, N., Porcar-Castell, A., ... & Atherton, J., 2020 Diurnal response of sun-induced fluorescence and PRI to water stress in maize using a near-surface remote sensing platform, Xu, S., Liu, Z., Zhao, L., Zhao, H., & Ren, S., 2018 Proximal and remote sensing in plant phenomics: Twenty years of progress, challenges and perspectives, Tao, H., Xu, S., Tian, Y., Li, Z., Ge, Y., Zhang, J., ... & Jin, S., 2022 Retrieving the diurnal FPAR of a maize canopy from the jointing stage to the tasseling stage with vegetation indices under different water stresses and light conditions, Zhao, L., Liu, Z., Xu, S., He, X., Ni, Z., Zhao, H., & Ren, S., 2018 An automated comparative observation system for sun-induced chlorophyll fluorescence of vegetation canopies, Zhou, X., Liu, Z., Xu, S., Zhang, W., & Wu, J., 2016 Is satellite Sun-Induced Chlorophyll Fluorescence more indicative than vegetation indices under drought condition?, Cao, J., An, Q., Zhang, X., Xu, S., Si, T., & Niyogi, D., 2021 Using High-Frequency PAR Measurements to Assess the Quality of the SIF Derived from Continuous Field Observations, Han, S., Liu, Z., Chen, Z., Jiang, H., Xu, S., Zhao, H., & Ren, S., 2022 Using the diurnal variation characteristics of effective quantum yield of PSII photochemistry for drought stress detection in maize, Chen, Z., Liu, Z., Han, S., Jiang, H., Xu, S., Zhao, H., & Ren, S., 2022 Simultaneous prediction of wheat yield and grain protein content using multitask deep learning from time-series proximal sensing, Sun, Z., Li, Q., Jin, S., Song, Y., Xu, S., Wang, X., ... & Jiang, D., 2022 What does the NDVI really tell us about crops? Insight from proximal spectral field sensors, Atherton, J., Zhang, C., Oivukkamäki, J., Kulmala, L., Xu, S., Hakala, T., ... & Porcar-Castell, A., 2022 Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing, Li, Q., Jin, S., Zang, J., Wang, X., Sun, Z., Li, Z., Xu, S., Ma, Q., … & Jiang, D., 2022 Assessing the response of satellite sun-induced chlorophyll fluorescence and MODIS vegetation products to soil moisture from 2010 to 2017: a case in Yunnan Province of China, Ni, Z., Huo, H., Tang, S., Li, Z. L., Liu, Z., Xu, S., & Chen, B., 2019

Areas of Focus

植被定量遥感与农作物生长监测
基于光谱的农作物生理表型提取

Publications

Exploring the Sensitivity of Solar-Induced Chlorophyll Fluorescence at Different Wavelengths in Response to Drought, Xu, S., Liu, Z., Han, S., Chen, Z., He, X., Zhao, H., & Ren, S., 2023
Structural and photosynthetic dynamics mediate the response of SIF to water stress in a potato crop, Xu, S., Atherton, J., Riikonen, A., Zhang, C., Oivukkamäki, J., MacArthur, A., ... & Porcar-Castell, A., 2021
On the estimation of the leaf angle distribution from drone based photogrammetry, Xu, S., Zaidan, M. A., Honkavaara, E., Hakala, T., Viljanen, N., Porcar-Castell, A., ... & Atherton, J., 2020
Diurnal response of sun-induced fluorescence and PRI to water stress in maize using a near-surface remote sensing platform, Xu, S., Liu, Z., Zhao, L., Zhao, H., & Ren, S., 2018
Proximal and remote sensing in plant phenomics: Twenty years of progress, challenges and perspectives, Tao, H., Xu, S., Tian, Y., Li, Z., Ge, Y., Zhang, J., ... & Jin, S., 2022
Retrieving the diurnal FPAR of a maize canopy from the jointing stage to the tasseling stage with vegetation indices under different water stresses and light conditions, Zhao, L., Liu, Z., Xu, S., He, X., Ni, Z., Zhao, H., & Ren, S., 2018
An automated comparative observation system for sun-induced chlorophyll fluorescence of vegetation canopies, Zhou, X., Liu, Z., Xu, S., Zhang, W., & Wu, J., 2016
Is satellite Sun-Induced Chlorophyll Fluorescence more indicative than vegetation indices under drought condition?, Cao, J., An, Q., Zhang, X., Xu, S., Si, T., & Niyogi, D., 2021
Using High-Frequency PAR Measurements to Assess the Quality of the SIF Derived from Continuous Field Observations, Han, S., Liu, Z., Chen, Z., Jiang, H., Xu, S., Zhao, H., & Ren, S., 2022
Using the diurnal variation characteristics of effective quantum yield of PSII photochemistry for drought stress detection in maize, Chen, Z., Liu, Z., Han, S., Jiang, H., Xu, S., Zhao, H., & Ren, S., 2022
Simultaneous prediction of wheat yield and grain protein content using multitask deep learning from time-series proximal sensing, Sun, Z., Li, Q., Jin, S., Song, Y., Xu, S., Wang, X., ... & Jiang, D., 2022
What does the NDVI really tell us about crops? Insight from proximal spectral field sensors, Atherton, J., Zhang, C., Oivukkamäki, J., Kulmala, L., Xu, S., Hakala, T., ... & Porcar-Castell, A., 2022
Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing, Li, Q., Jin, S., Zang, J., Wang, X., Sun, Z., Li, Z., Xu, S., Ma, Q., … & Jiang, D., 2022
Assessing the response of satellite sun-induced chlorophyll fluorescence and MODIS vegetation products to soil moisture from 2010 to 2017: a case in Yunnan Province of China, Ni, Z., Huo, H., Tang, S., Li, Z. L., Liu, Z., Xu, S., & Chen, B., 2019

遥感植被作物生长监测光谱分析表型提取生理定量农业传感技术

周济 - 南京农业大学

Area of Focus
表型组学 | 人工智能 | 基因组学 | 育种 | 作物 | 数据融合 | 机器学习 | 遥感 | 农业 | 创新

刘守阳 - 南京农业大学

Area of Focus
表型 | 算法 | 大田作物 | 基因-环境互作 | 三维模型 | 遥感 | 监测 | 高通量 | 作物性状 | 融合

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